Search

Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m −2 yr −1 , which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m −2 yr −1 on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m −2 yr −1 , respectively, in agreement with observed average GPP (1230 g C m −2 yr −1 ) and RECO (1046 g C m −2 yr −1 ). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was ∼5–16 g C week −1 across the study sites, while the goodness of fit (R 2 ) was ∼0.4–0.9. For evapotranspiration (ET), the average value of simulated ET (415 mm yr −1 ) for all sites and years is close to the average value of the observed ET (451 mm yr −1 ) by flux towers (on a weekly basis, RMSE∼2–8 mm week −1 ; R 2 = 0.3–0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system. [ABSTRACT FROM AUTHOR]